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Background <p>We aimed to develop a machine learning model based on intratumoral and peritumoral <sup>18</sup>F-FDG PET/CT radiomics to non-invasively and dynamically predict the response to immunotherapy in non-small cell lung cancer (NSCLC).</p> Methods <p>This retrospective study included 296 NSCLC patients, including a training cohort (N = 183), a testing cohort (N = 78), and a TCIA radiogenomic cohort (N = 35). The extreme gradient boosting algorithm was employed to develop the radiomic models.</p> Results <p>The COMB-Radscore, which was developed by combining radiomic features from PET, CT, and PET/CT images, had the most satisfactory predictive performance with AUC (ROC) 0.894 and 0.819 in the training and testing cohorts, respectively. Survival analysis has demonstrated that COMB-Radscore is an independent prognostic factor for progression-free survival and overall survival. Moreover, COMB-Radscore demonstrates excellent dynamic predictive performance, with an AUC (ROC) of 0.857, enabling the earlier detection of potential disease progression in patients compared to radiological evaluation solely relying on tumor size. Further radiogenomic analysis showed that the COMB-Radscore was associated with infiltration abundance and functional status of CD8 + T cells.</p> Conclusions <p>The radiomic model holds promise as a precise, personalized, and dynamic decision support tool for the treatment of NSCLC patients.</p>

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Intratumoral and peritumoral PET/CT-based radiomics for non-invasively and dynamically predicting immunotherapy response in NSCLC

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月10日

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  • Xianwen Lin,
  • Zhiwei Liu,
  • Kun Zhou,
  • Yuedan Li,
  • Genjie Huang,
  • Hao Zhang,
  • Tingting Shu,
  • Zhenhua Huang,
  • Yuanyuan Wang,
  • Wei Zeng,
  • Yulin Liao,
  • Jianping Bin,
  • Min Shi,
  • Wangjun Liao,
  • Wenlan Zhou,
  • Na Huang

摘要

Background

We aimed to develop a machine learning model based on intratumoral and peritumoral 18F-FDG PET/CT radiomics to non-invasively and dynamically predict the response to immunotherapy in non-small cell lung cancer (NSCLC).

Methods

This retrospective study included 296 NSCLC patients, including a training cohort (N = 183), a testing cohort (N = 78), and a TCIA radiogenomic cohort (N = 35). The extreme gradient boosting algorithm was employed to develop the radiomic models.

Results

The COMB-Radscore, which was developed by combining radiomic features from PET, CT, and PET/CT images, had the most satisfactory predictive performance with AUC (ROC) 0.894 and 0.819 in the training and testing cohorts, respectively. Survival analysis has demonstrated that COMB-Radscore is an independent prognostic factor for progression-free survival and overall survival. Moreover, COMB-Radscore demonstrates excellent dynamic predictive performance, with an AUC (ROC) of 0.857, enabling the earlier detection of potential disease progression in patients compared to radiological evaluation solely relying on tumor size. Further radiogenomic analysis showed that the COMB-Radscore was associated with infiltration abundance and functional status of CD8 + T cells.

Conclusions

The radiomic model holds promise as a precise, personalized, and dynamic decision support tool for the treatment of NSCLC patients.